SoroushVahidi/scidocs huggingface dataset:
Aspect based sentiment analysis in social media with classifier ensembles
(2017)

The analysis of user generated content on social media and the accurate specification of user opinions towards products and events is quite valuable to many applications. With the proliferation of Web 2.0 and the rapid growth of user-generated content on the web, approaches on aspect level sentiment analysis that yield fine grained information are of great interest. In this work, a classifier ensemble approach for aspect based sentiment analysis is presented. The approach is generic and utilizes latent dirichlet allocation to model a topic and to specify the main aspects that users address. Then, each comment is further analyzed and word dependencies that indicate the interactions between words and aspects are extracted. An ensemble classifier formulated by naive bayes, maximum entropy and support vector machines is designed to recognize the polarity of the user's comment towards each aspect. The evaluation results show sound improvement compared to individual classifiers and indicate that the ensemble system is scalable and accurate in analyzing user generated content and in specifying users' opinions and attitudes.
Bioaccumulation of nutrients and metals in sediment, water, and phoomdi from Loktak Lake (Ramsar site), northeast India: phytoremediation options and risk assessment.
(2016)

In order to determine the potential of phoomdi to accumulate nutrients and metals, 11 dominant species belonging to 10 different families, sediment, and water were analyzed for a period of 2 years from the largest freshwater wetland of north-east India, Loktak (Ramsar site). Results revealed nutrient (TN and TP) and metal (Fe, Mn, Zn, and Cu) compartmentalization in the order phoomdi > sediment > water. Iron concentrations in water (0.37 ± 0.697 to 0.57 ± 1.010 mg L(-1)) and sediments (81.8 ± 0.45 to 253.1 ± 0.51 mg kg(-1)) show high metal discharge into the wetland. Metal accumulation in phoomdi ranged up to 212.3 ± 0.46-9461.4 ± 1.09 mg kg(-1) for Fe; 85.9 ± 0.31-3565.1 ± 0.87 mg kg(-1) for Mn; 9.6 ± 0.41-85.39 ± 0.58 mg kg(-1) for Zn; and 0.31 ± 0.04-9.2 ± 0.04 mg kg(-1) for Cu, respectively. High bioaccumulation factors (BAF) for metals (S. cucullata, 5.8 × 10(4) Fe, 3.9 × 10(4) Mn, and 1.7 × 10(4) Cu, and O. javanica, 4.9 × 10(3) Zn) and nutrients (S. polyrhiza, 9.7 × 10(2) TN, and Z. latifolia, 7.9 × 10(4) TP) revealed good accumulation in phoomdi compared to the wetland water column and indicate their potential to maintain a safe environment of Loktak. Further, the paper analyzed the health hazard of metals via phoomdi wild edible consumption, with the results confirming potential risk. Thus, the paper showed the need of in-depth monitoring and ample management strategies to ensure nutritional safety conditions of locals from the metals.
Me , myself , and lie : The role of self-awareness in deception
(2005)

Deception has been studied extensively but still little is known about individual differences in deception ability. We investigated the relationship between self-awareness and deception ability. We enlisted novice actors to portray varying levels of deception. Forty-two undergraduates viewed the videotaped portrayals and rated the actors believability. Actors with high private self-awareness were more effective deceivers, suggesting that high self-monitors are more effective at deceiving. Self-awareness may lead to knowledge of another s mental state (i.e., Theory of Mind), which may improve an individual s deception ability. 2005 Elsevier Ltd. All rights reserved.
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
(2017)

Spatial item recommendation has become an important means to help people discover interesting locations, especially when people pay a visit to unfamiliar regions. Some current researches are focusing on modelling individual and collective geographical preferences for spatial item recommendation based on users' check-in records, but they fail to explore the phenomenon of user interest drift across geographical regions, i.e., users would show different interests when they travel to different regions. Besides, they ignore the influence of public comments for subsequent users' check-in behaviors. Specifically, it is intuitive that users would refuse to check in to a spatial item whose historical reviews seem negative overall, even though it might fit their interests. Therefore, it is necessary to recommend the right item to the right user at the right location. In this paper, we propose a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-town and out-of-town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews. Due to the sparsity of user activities in out-of-town regions, LSARS is further designed to incorporate the public preferences learned from local users' check-in behaviors. Finally, we deploy LSARS into two practical application scenes: spatial item recommendation and target user discovery. Extensive experiments on two large-scale location-based social networks (LBSNs) datasets show that LSARS achieves better performance than existing state-of-the-art methods.
HF , VHF , and UHF Systems and Technology
(2001)

A wide variety of unique systems and components inhabits the HF, VHF, and UHF bands. Many communication systems (ionospheric, meteor-burst, and troposcatter) provide beyond-line-of-sight coverage and operate independently of external infrastructure. Broadcasting and over-the-horizon radar also operate in these bands. Magnetic-resonance imaging uses HF/VHF signals to see the interior of a human body, and RF heating is used in a variety of medical and industrial applications. Receivers typically employ a mix of analog and digital-signal-processing techniques. Systems for these frequencies make use of RF-power MOSFETs, p-i-n diodes, and ferrite-loaded transmission-line transformers.
Experimental study of desalination using direct contact membrane distillation : a new approach to flux enhancement
(2003)

New membrane distillation configurations and a new membrane module were investigated to improve water desalination. The performances of three hydrophobic microporous membranes were evaluated under vacuum enhanced direct contact membrane distillation (DCMD) with a turbulent flow regime and with a feed water temperature of only 40 ◦C. The new configurations provide reduced temperature polarization effects due to better mixing and increased mass transport of water due to higher permeability through the membrane and due to a total pressure gradient across the membrane. Comparison with previously reported results in the literature reveals that mass transport of water vapors is substantially improved with the new approach. The performance of the new configuration was investigated with both NaCl and synthetic sea salt feed solutions. Salt rejection was greater than 99.9% in almost all cases. Salt concentrations in the feed stream had only a minor effect on water flux. The economic aspects of the enhanced DCMD process are briefly discussed and comparisons are made with the reverse osmosis (RO) process for desalination. © 2003 Elsevier B.V. All rights reserved.
Dynamic hand gesture recognition using hidden Markov models
(2012)

Hand gesture has become a powerful means for human-computer interaction. Traditional gesture recognition just consider hand trajectory. For some specific applications, such as virtual reality, more natural gestures are needed, which are complex and contain movement in 3-D space. In this paper, we introduce an HMM-based method to recognize complex single hand gestures. Gesture images are gained by a common web camera. Skin color is used to segment hand area from the image to form a hand image sequence. Then we put forward a state-based spotting algorithm to split continuous gestures. After that, feature extraction is executed on each gesture. Features used in the system contain hand position, velocity, size, and shape. We raise a data aligning algorithm to align feature vector sequences for training. Then an HMM is trained alone for each gesture. The recognition results demonstrate that our methods are effective and accurate.
Human Action Recognition Using Multi-Velocity STIPs and Motion Energy Orientation Histogram
(2014)

Local image features in space-time or spatio-temporal interest points provide compact and abstract representations of patterns in a video sequence. In this paper, we present a novel human action recognition method based on multi-velocity spatio-temporal interest points (MVSTIPs) and a novel local descriptor called motion energy (ME) orientation histogram (MEOH). The MVSTIP detection includes three steps: first, filtering video frames with multi-direction ME filters at different speeds to detect significant changes at the pixel level; thereafter, a surround suppression model is employed to rectify the ME deviation caused by the camera motion and complicated backgrounds (e.g., dynamic texture); finally, MVSTIPs are obtained with local maximum filters at multispeeds. After detection, we develop MEOH descriptor to capture the motion features in local regions around interest points. The performance of the proposed method is evaluated on KTH, Weizmann, and UCF sports human action datasets. Results show that our method is robust to both simple and complex backgrounds and the method is superior to other methods that are based on local features.
Pixelwise View Selection for Unstructured Multi-View Stereo
(2016)

This work presents a Multi-View Stereo system for robust and efficient dense modeling from unstructured image collections. Our core contributions are the joint estimation of depth and normal information, pixelwise view selection using photometric and geometric priors, and a multi-view geometric consistency term for the simultaneous refinement and image-based depth and normal fusion. Experiments on benchmarks and large-scale Internet photo collections demonstrate stateof-the-art performance in terms of accuracy, completeness, and efficiency. Fig. 1. Reconstructions for Louvre, Todai-ji, Paris Opera, and Astronomical Clock.
Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm
(2019)

Nowadays with a growing number of online controlling systems in the organization and also a high demand of monitoring and stats facilities that uses data streams to log and control their subsystems, data stream mining becomes more and more vital. Hoeffding Trees (also called Very Fast Decision Trees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for classification and regression problems showed good performance in handling facing challenges and making the possibility of any-time prediction. Although these methods outperform other methods e.g. Artificial Neural Networks (ANN) and Support Vector Regression (SVR), they suffer from high latency in adapting with new concepts when the statistical distribution of incoming data changes. In this article, we introduced a new algorithm that can detect and handle concept drift phenomenon properly. This algorithms also benefits from fast startup ability which helps systems to be able to predict faster than other algorithms at the beginning of data stream arrival. We also have shown that our approach will overperform other controversial approaches for classification and regression tasks.
An induction furnace employing with half bridge series resonant inverter
(2014)

In this paper, an induction furnace employing with half bridge series resonant inverter built around IGBT as its switching devices suitable for melting 500 grams of brass is presented. Melting times of 10 minutes were achieved at a power level of approximately 4 kW. The operating frequency is automatically tracked to maintain a small constant lagging phase angle using dual phase lock loop when brass is melted. The coil voltage is controlled to protect the resonant capacitors. The experimental results are presented.
Improving deep learning performance using random forest HTM cortical learning algorithm
(2018)

Deep Learning is an artificial intelligence function that imitates the mechanisms of the human mind in processing records and developing shapes to be used in selection construction. The objective of the paper is to improve the performance of the deep learning using a proposed algorithm called RFHTMC. This proposed algorithm is a merged version from Random Forest and HTM Cortical Learning Algorithm. The methodology for improving the performance of Deep Learning depends on the concept of minimizing the mean absolute percentage error which is an indication of the high performance of the forecast procedure. In addition to the overlap duty cycle which its high percentage is an indication of the speed of the processing operation of the classifier. The outcomes depict that the proposed set of rules reduces the absolute percent errors by using half of the value. And increase the percentage of the overlap duty cycle with 15%.
Scientific Paper Summarization Using Citation Summary Networks
(2008)

Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a model of summarizing a single article, which can be further used to summarize an entire topic. Our model is based on analyzing others’ viewpoint of the target article’s contributions and the study of its citation summary network using a clustering approach.
Finding Deceptive Opinion Spam by Any Stretch of the Imagination
(2011)

Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam—fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.
Transductive Inference for Text Classification using Support Vector Machines
(1999)

This paper introduces Transductive Support Vector Machines (TSVMs) for text classi cation. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimize misclassi cations of just those particular examples. The paper presents an analysis of why TSVMs are well suited for text classi cation. These theoretical ndings are supported by experiments on three test collections. The experiments show substantial improvements over inductive methods, especially for small training sets, cutting the number of labeled training examples down to a twentieth on some tasks. This work also proposes an algorithm for training TSVMs e ciently, handling 10,000 examples and more.
Thumbs up? Sentiment Classification using Machine Learning Techniques
(2002)

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging. Publication info: Proceedings of EMNLP 2002, pp. 79–86.
Judging Grammaticality with Tree Substitution Grammar Derivations
(2011)

In this paper, we show that local features computed from the derivations of tree substitution grammars — such as the identify of particular fragments, and a count of large and small fragments — are useful in binary grammatical classification tasks. Such features outperform n-gram features and various model scores by a wide margin. Although they fall short of the performance of the hand-crafted feature set of Charniak and Johnson (2005) developed for parse tree reranking, they do so with an order of magnitude fewer features. Furthermore, since the TSGs employed are learned in a Bayesian setting, the use of their derivations can be viewed as the automatic discovery of tree patterns useful for classification. On the BLLIP dataset, we achieve an accuracy of 89.9% in discriminating between grammatical text and samples from an n-gram language model.
Code Review Quality: How Developers See It
(2016)

In a large, long-lived project, an effective code review process is key to ensuring the long-term quality of the code base. In this work, we study code review practices of a large, open source project, and we investigate how the developers themselves perceive code review quality. We present a qualitative study that summarizes the results from a survey of 88 Mozilla core developers. The results provide developer insights into how they define review quality, what factors contribute to how they evaluate submitted code, and what challenges they face when performing review tasks. We found that the review quality is primarily associated with the thoroughness of the feedback, the reviewer's familiarity with the code, and the perceived quality of the code itself. Also, we found that while different factors are perceived to contribute to the review quality, reviewers often find it difficult to keep their technical skills up-to-date, manage personal priorities, and mitigate context switching.
EPOCHS: a platform for agent-based electric power and communication simulation built from commercial off-the-shelf components
(2006)

This paper reports on the development and subsequent use of the electric power and communication synchronizing simulator (EPOCHS), a distributed simulation environment. Existing electric power simulation tools accurately model power systems of the past, which were controlled as large regional power pools without significant communication elements. However, as power systems increasingly turn to protection and control systems that make use of computer networks, these simulators are less and less capable of predicting the likely behavior of the resulting power grids. Similarly, the tools used to evaluate new communication protocols and systems have been developed without attention to the roles they might play in power scenarios. EPOCHS integrates multiple research and commercial off-the-shelf systems to bridge the gap.
Closed-form Solution for IMU based LSD-SLAM Point Cloud Conversion into the Scaled 3D World Environment
(2017)

SLAM is a very popular research stream in computer vision and robotics nowadays. For more effective SLAM implementation it is necessary to have reliable information about the environment, also the data should be aligned and scaled according to the real world coordinate system. Monocular SLAM research is an attractive sub-stream, because of the low equipment cost, size and weight. In this paper we present a way to build a conversion from LSD-SLAM coordinate space to the real world coordinates using a true metric scale with IMU sensor data implementation. The causes of differences between the real and calculated spaces are explained and the possibility of conversions between the spaces is proved. Additionally, a closed-form solution for inter space transformation calculation is presented. The synthetic method of generating high level accurate and well controlled input data for the LSD-SLAM algorithm is presented. Finally, the reconstructed 3D environment representation is delivered as an output of the implemented conversion.
Scalable Iterative Classification for Sanitizing Large-Scale Datasets
(2017)

Cheap ubiquitous computing enables the collection of massive amounts of personal data in a wide variety of domains. Many organizations aim to share such data while obscuring features that could disclose personally identifiable information. Much of this data exhibits weak structure (e.g., text), such that machine learning approaches have been developed to detect and remove identifiers from it. While learning is never perfect, and relying on such approaches to sanitize data can leak sensitive information, a small risk is often acceptable. Our goal is to balance the value of published data and the risk of an adversary discovering leaked identifiers. We model data sanitization as a game between 1) a publisher who chooses a set of classifiers to apply to data and publishes only instances predicted as non-sensitive and 2) an attacker who combines machine learning and manual inspection to uncover leaked identifying information. We introduce a fast iterative greedy algorithm for the publisher that ensures a low utility for a resource-limited adversary. Moreover, using five text data sets we illustrate that our algorithm leaves virtually no automatically identifiable sensitive instances for a state-of-the-art learning algorithm, while sharing over 93 percent of the original data, and completes after at most five iterations.
The diverse CB1 and CB2 receptor pharmacology of three plant cannabinoids: delta9-tetrahydrocannabinol, cannabidiol and delta9-tetrahydrocannabivarin.
(2008)

Cannabis sativa is the source of a unique set of compounds known collectively as plant cannabinoids or phytocannabinoids. This review focuses on the manner with which three of these compounds, (-)-trans-delta9-tetrahydrocannabinol (delta9-THC), (-)-cannabidiol (CBD) and (-)-trans-delta9-tetrahydrocannabivarin (delta9-THCV), interact with cannabinoid CB1 and CB2 receptors. Delta9-THC, the main psychotropic constituent of cannabis, is a CB1 and CB2 receptor partial agonist and in line with classical pharmacology, the responses it elicits appear to be strongly influenced both by the expression level and signalling efficiency of cannabinoid receptors and by ongoing endogenous cannabinoid release. CBD displays unexpectedly high potency as an antagonist of CB1/CB2 receptor agonists in CB1- and CB2-expressing cells or tissues, the manner with which it interacts with CB2 receptors providing a possible explanation for its ability to inhibit evoked immune cell migration. Delta9-THCV behaves as a potent CB2 receptor partial agonist in vitro. In contrast, it antagonizes cannabinoid receptor agonists in CB1-expressing tissues. This it does with relatively high potency and in a manner that is both tissue and ligand dependent. Delta9-THCV also interacts with CB1 receptors when administered in vivo, behaving either as a CB1 antagonist or, at higher doses, as a CB1 receptor agonist. Brief mention is also made in this review, first of the production by delta9-THC of pharmacodynamic tolerance, second of current knowledge about the extent to which delta9-THC, CBD and delta9-THCV interact with pharmacological targets other than CB1 or CB2 receptors, and third of actual and potential therapeutic applications for each of these cannabinoids.